Increasingly prevalent computers, mobile phones, smart devices, appliances, and other devices, require a variety of programs operating in tandem. However, these programs may comprise errors and defects, which if not identified and corrected in time may lead to malfunctioning of the program itself and/or other related programs and the devices that run them. However, in conventional systems, the testing of program code primarily relies on debugging code and/or data validation. First, this conventional testing process, however, is not compatible for testing programs having complex structures such as machine-learning programs or deep-learning programs which have multiple layers of neural networks that do not lend themselves to the conventional methods. Second, conventional methods are not configured for identifying root causes of inaccuracies, particularly in the case of machine-learning programs or deep-learning programs, thereby precluding any accurate/precise corrections of the program code to rectify the defects. Third, the conventional testing processes are heavily reliant on test cases for testing programs, and may not be able to identify defects when other use cases are provided to the program. Conventional testing methods are most likely to designate insufficiency of model training data or test data as the cause for errors in machine-learning programs or deep-learning programs, even if the program code itself is erroneous (e.g., program code may comprise architectural configuration issues, interference state issues, or implementation issues, etc. that are causing the errors/inaccuracies in reality). Particularly in the case of machine-learning programs or deep-learning programs, an error/bug cannot be determined in conventional testing systems through a few instances of inaccuracies during testing using test cases, because machine-learning programs or deep-learning programs are inherently constantly evolving (e.g., their accuracies improve over-time). Moreover, this conventional process while not being reliably accurate is also time intensive and laborious. Therefore, a need exists for a novel system that overcomes the foregoing shortcomings of conventional system.
The previous discussion of the background to the invention is provided for illustrative purposes only and is not an acknowledgement or admission that any of the material referred to is or was part of the common general knowledge as at the priority date of the application.